Customer experience analysis, or CX analysis, involves studying the ways in which potential and existing customers interact with your brand. Learning how your customers behave, what they like and dislike about your brand, and many other key details can give you the information needed to build customer loyalty and even attract more customers.
There are many ways to perform a customer experience analysis. For example, a customer experience website analysis could tell you which product pages your customers are frequenting most, how much time they spend on certain pages, which items are getting abandoned in shopping carts, and many more important details.
But when it comes to truly understanding the mind of the customer, surveys are the best CX analysis tool for soliciting direct feedback. Customer surveys can produce a whole host of valuable CX data analytics, including Customer Satisfaction (CSAT) Scores and Net Promoter Scores (NPS).
However, if you really want to understand how your customers are feeling, you should go beyond just quantitative CX data analytics and dive into sentiment analysis. Why? Read on to find out…
What is sentiment analysis?
Sentiment analysis (sometimes also referred to as opinion mining) is a technique that involves using natural language processing (NLP) to determine whether subjective feedback is negative, neutral, or positive.
When it comes to customer experience analysis, most sentiment analysis will be performed on open-ended responses to customer survey questions.
For example, let’s say you send out a customer satisfaction survey that ends by asking participants if they have any other thoughts or comments they would like to share. A survey platform like Sogolytics would automatically perform a sentiment analysis on the open-ended responses, creating a “Sentiment Graph” in the Text Analysis Report that categorizes all responses into topics.
When you hover over each topic’s chart, you will see the percentage of positive, neutral, and negative responses, as well as the overall mean and median sentiment.
This automatic, real-time reporting is obviously much faster and more efficient than reading through every survey response yourself and keeping count of positive comments versus negative ones. It’s an advanced form of customer experience data analysis that can yield valuable, actionable insights to improve CX.
How can sentiment analysis be used to improve customer experience?
In some ways, the advantage of studying customer sentiment should be obvious. After all, what better way to perform customer experience analysis than to hear it straight from the horse’s mouth?
However, to get a clearer, more concrete understanding of the benefits, let’s look at some specific examples of ways that sentiment analysis can contribute to an enhanced customer experience.
1. Identify strengths and weaknesses of your existing customer experience
The biggest impact of sentiment analysis is that it allows you to identify both the positive and negative aspects of the customer experience you provide.
Let’s say your customer satisfaction survey includes several open-ended questions about a variety of topics. A sentiment analysis of all the responses may reveal that many respondents are using negative words (“useless,” “awful,” “frustrating”) to describe your call center, indicating that customer support is an area in need of improvement. Or, alternately, they might use positive words (“fantastic,” “intuitive,” “easy to use”) to describe a specific product or feature, revealing it as one of your strengths.
You can perform sentiment analysis on any open-ended feedback, meaning it can be used to perform a wide range of customer experience analysis. Customer sentiment can (and should) influence product improvements, website changes, customer service initiatives, and many other aspects of CX.
2. Track customer sentiment over time
As you continue to make sentiment analysis a key part of your overall CX analysis strategy, you’ll build up weeks, months, and years of data. By continually tracking customer sentiment, you can see how well you’re performing/being perceived over time.
If customer sentiment is slowly declining in one area, you may need to dig into the CX data analytics to uncover the issue. If customer sentiment is quickly declining, you should definitely address the issue with a real sense of urgency.
If you see any noticeable changes (for good or bad) in customer sentiment over a certain period of time, you can investigate further to find out what you were doing right (or wrong) and learn from the data.
3. Follow up with unhappy customers
An important step in customer experience analysis is not just studying and understanding detractors, but trying your best to turn them into promoters.
You can use sentiment analysis to identify unhappy customers and reach out to them. If a customer complains about poor service or a product issue, send them an apology email and perhaps a discount code or special offer as a consolation.
It’s important to have a sense of urgency when it comes to making amends with unsatisfied customers. Sentiment analysis can help you identify the customers who are the most at risk of churning so that you can quickly step in and try to rescue the relationship.
Taking a holistic approach to customer experience analysis
Sentiment analysis is an important component of CX analysis, but it’s still just one part of it. There are many metrics you’ll want to regularly monitor and analyze if you are serious about improving the customer experience. If you take the right approach to collecting and analyzing customer feedback, you’ll have all of the CX data analytics you need to inform a better approach to customer experience.
Wondering where to get started with customer experience analysis? We’re here to support your efforts!
